博碩士論文 942205009 詳細資訊


姓名 高欣如(Xin-ru Kao)  查詢紙本館藏   畢業系所 統計研究所
論文名稱 Cox 比例風險假設之探討與擴充風險模型之應用
(Discussion on Cox Proportional Hazards Assumption and Application of Extended Hazard Model)
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摘要(中) 存活分析中, Cox 比例風險模型(Cox proportional hazards model) 最常被用來描述變數與存活資訊間的關係。然而,我們需進一步地評估模型的正當性,也就是必須符合比例風險假設(proportional hazards assumption),方能利用 Cox 比例風險模型來配適資料。一個令人感興趣的問題是檢定比例風險假設是否有足夠的證據說明 Cox 比例風險模型可以配適資料配適的很好。另一方面,當比例風險假設不成立時,使用 Cox 比例風險模型是不合理的,因此,加速失敗時間 (accelerated failure time) 模型是另一個選擇,可以使用此模型來代替 Cox 模型。然而,在有時間相依(time-dependent) 共變數 (covariates) 之下的加速失敗時間模型,沒有一個簡單的方法可以檢驗加速失敗時間模型是否可以合理的配適資料。在此我們將介紹一個更廣義的模型,稱為擴充風險模型 (extended hazard model),此模型包含了 Cox 比例風險模型及加速失敗時間模型,可以用來解決上述的問題。因為 Cox 比例風險模型及加速失敗時間模型是擴充風險模型的特例,藉由此特性可以將此模型視為完整模型 (full model) ,而 Cox 比例風險模型及加速失敗時間模型視為簡約模型 (reduced model) 做概似比檢定(likelihood ratio test) 來決定用何種模型來配適存活資料。最後,以台灣愛滋病 (HIV/AIDS) 病患的資料證明可以使用擴充風險模型做模型的檢定, 選擇適當的模型。
摘要(英) The Cox proportional hazards model has been widely used to describe the relationship between survival information and covariates. The validity to apply the Cox model for data is usually based on checking the proportional hazards assumption. It’s an interesting problem to investigate whether checking this assumption is sufficient as an evidence to fit data with the Cox model. On the other hand, when proportional hazards assumption fails, the Accelerated Failure Time (AFT) model is a popular alternative to the Cox model. However, when data include time-dependent covariates there are no convenient tools to check if AFT is appropriate for the data. An general class model termed “extended hazard model”, which contains the Cox and AFT models as its special case may be helpful to study the above problems. Because under the nested structure, we may test the fit of Cox and AFT models for data. Finally, we demonstrate the new model through a case study of Taiwanese HIV/AIDS cohort data.
關鍵字(中) ★ 比例風險假設
★ 擴充風險模型
★ 長期追蹤資料
★ Schoenfeld 殘差
★ Cox 比例風險模型
關鍵字(英) ★ Proportional hazards assumption
★ Cox proportional hazards model
★ Schoenfeld residual
★ Longitudinal data
★ Extended hazard model
論文目次 摘要 i
Abstract ii
誌謝辭 iii
目錄 v
圖目錄 vii
表目錄 viii
第一章緒論 1
1.1 研究背景及動機. . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 本文架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
第二章統計方法 10
2.1 線性混合隨機效應模型. . . . . . . . . . . . . . . . . . . . . 11
2.2 Cox 比例風險模型. . . . . . . . . . . . . . . . . . . . . . . 12
2.3 加速失敗時間模型. . . . . . . . . . . . . . . . . . . . . . . . 13
2.4 擴充風險模型. . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.5 Schoenfeld 殘差. . . . . . . . . . . . . . . . . . . . . . . . 16
第三章統計模擬 19
3.1 模擬方法. . . . . . . . . . . . . . . . . . . . . . . . . . . .20
3.2 模擬結果. . . . . . . . . . . . . . . . . . . . . . . . . . . .23
第四章實例研究 25
4.1 台灣愛滋病病患資料與背景. . . . . . . . . . . . . . . . . . . 26
4.2 實例分析. . . . . . . . . . . . . . . . . . . . . . . . . . . .29
第五章結論與討論 34
參考文獻 36
參考文獻 [1] Andersen, P.K. (1982). “Testing Goodness-of-Fit of Cox’s Regression and Life Model.” Biometrics, 38, 67-77.
[2] Breslow, N.E., Edler, L. and Berger, J. (1984). “A two-sample censored data rank test for acceleration.” Biometrics, 40, 1049-1062.
[3] Chen, Y.Q. and Jewell, N.P. (2001). “On a general class of semiparametric hazards regression models.” Biometrika B, 88, 687-702.
[4] Ciampi, A. and Etezadi-Amoli, J. (1985). “A general model for testing the proportional hazards and the accelerated failure time hypothesis in the analysis of censored survival data with covariate.” Communications
in Statistics, 14, 651-667.
[5] Cox, D.R. (1972). “Regression models and life-tables (with Discussion).” Journal of the Royal Statistical Society, Series B 34, 187-220.
[6] Cox, D.R. (1979). “A Note on the Graphical Analysis of Survival Data.” Biometrics, 66, 188-190.
[7] Cox, D.R. and Snell, E.J. (1968). “A General Definition of Residuals.” Journal of the Royal Statistical Society, Ser. B 30, 248-275.
[8] Chen, Y.Q. and Jewell, N.P. (2001). “On a general class of semiparametric hazards regression models.” Biometrika, 88, 687-702.
[9] Egger, M., Hirschel, B., Francioli, P. et al. (1997). “Impact of new antiretroviral combination therapies in HIV infected patients in Switzerland: prospective multicentre study.” Swiss HIV Cohort Study BMJ, 315(7117),
1194-9.
[10] Etezadi-Amoli, J. and Ciampi, A. (1987). “Extended hazard regression for censored survival data with covariates: A spline approximation for the baseline hazard function.” Biometrics, 43, 191-192.
[11] Grambsch, P.M. and Therneau, T.M. (1994). “Proportional hazards tests and diagnostics based on weighted residuals.” Biometrics, 81, 515-526.
[12] Gulick, R.M., Meibohm, A., Havlir, D., et al (2003). “Six-year follow-up of HIV-1-infected adults in a clinical trial of antiretroviral therapy with indinavir, zidovudine, and lamivudine.” AIDS, 17, 2345-2349.
[13] Henderson, R., Diggle, P. and Dobson, A. (2000). “Joint modeling of longitudinal measurements and event time data.” Biostatistics, 4, 465-480.
[14] Kaufmann, G.R., Perrink, L., Pantaleo, G., et al. (2003). “CD4 Tlymphocyte recovery in individuals with advanced HIV-1 infection receiving potent antiretroviral therapy for 4 years: the Swiss HIV Cohort Study.” Arch. Intern. Med., 163, 2187-2195.
[15] Kay, R. (1977). “Proportional Hazards Regression Models and the Analysis of Censored Survival Data.” Applied Statistics, 26, 227-237.
[16] Laird, N.M. and Ware, J.H. (1982). “Random-effects models for longitudinal data.” Biometrics, 38, 963-974.
[17] Lagakos, S.W. (1981). “The Graphical Evaluation of Explanatory Variables in Proportional Hazards Regression Models.”, Biometrika, 68, 93-98.
[18] Louis, T.A. (1991). “Nonparametric analysis of an accelerated failure time model.” Biometrika, 68, 381-390.
[19] Lucas, C.M., Chaisson, R.E. and Moore, R.D. (1999) “Highly active antiretroviral therapy in a large urban clinic: risk factors for virologic failure and adverse drug reactions.” Ann Intern Med, 131, 81-87.
[20] Miller, R.G. (1981). Survival Analysis. Wiley: New York.
[21] Moreau, T., O’Quigley, J. and Mesbah, M. (1985). “A Global Goodnessof-Fit Statistic for the Proportional Hazards Model.” Applied Statistics, 34, 212-218.
[22] O’Quigley, J. and Pessione, F. (1989). “Score Tests for Homogeneity of Regression Effect in the Proportional Hazards Model.” Biometrics, 45, 135-145.
[23] Palella, F.J., Delaney, K.M., Moorman, A.C., et al. (1998). “Declining morbidity and mortality among patients with advanced human immunod-eficiency virus infection. HIV Outpatient Study Investigators.” N Engl J Med., 338(13), 853-860.
[24] Pawitan, Y. and Self, S. (1993). “Modeling disease marker process in AIDS.” Journal of the American Statistical Association, 88, 719-726.
[25] Schoenfeld, D. (1980). “Chi-Squared Goodness-of-Fit Tests for the Proportional Hazards Regression Models.” Biometrika, 67, 145-153.
[26] Schoenfeld, D.A. (1982). “Partial residuals for the proportional hazards regression model.” Biometrika, 69, 239-241.
[27] Therneau, T.M. and Grambsch, P.M. (2000). Modeling Survival Data: Extending the Cox Model. Springer-Verlag.
[28] Tseng, Y.K., Hsieh, F. and Wang, J.L. (2005). “Joint modeling of accelerated failure time and longitudinal data.” Biometrika, 92, 587-603.
[29] Tsiatis, A.A., DeGruttola, V. and Wulfsohn, M.S. (1995). “Modeling the relationship of survival to longitudinal data measured with error. Applications to survival and CD4 counts in patients with AIDS.” Journal of the American Statistical Association, 90, 27-37.
[30] Tsiatis, A.A. and Davidian, M. (2001). “A semiparametric estimator for the proportional hazards model with longitudinal covariates measured with error.” Biometrika, 88, 446-458.
[31] Wang, Y. and Taylor, J.M.G. (2001). “Jointly Modeling Longitudinal and Event Time Data With Application to Acquired Immunodeficiency Syndrome.” J. Am. Statist. Assoc., 96, 895-905.
[32] Wulfsohn, M.S. and Tsiatis, A.A. (1997). “A Joint Model for Survival and Longitudinal Data Measured with Error.” Biometrics, 53, 330-339.
[33] Zeng, D. and Cai, J. (2005). “Asymptotic Results for Maximum Likelihood Estimators in Joint Analysis of Repeated Measurements and Survival Time.” The annals of Statistics, 33(5), 2132-2163.
指導教授 曾議寬(Yi-kuan Tseng) 審核日期 2009-6-25
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